> Markdown version of [/jobs/ext/4519202-data-analyst](https://www.wearedevelopers.com/jobs/ext/4519202-data-analyst). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst - **Company:** ACO, INC. - **Location:** Eleven Mile, AZ, United States - **Experience:** Experienced - **Salary:** $80,000.0 - $90,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Business Logic, Business Systems, Cloud Database, Information Systems, Data Cleansing, Information Engineering, Data Integration, Extract Transform Load (ETL), Document-Oriented Databases, Performance Tuning, Power BI, SQL Databases, Powerquery, Information Technology, Data Lineage, Data Analytics, Software Version Control, Data Pipelines - **Published:** November 2, 2026 - **Apply:** https://www.dice.com/job-detail/8c9f0c7b-3111-4442-ab73-0ec39a72ad18 ## About the Role Data Quality and Validation SQL and Data Modeling Data Integration and Mapping Power BI Reporting and Visualization Analytical Problem Solving Documentation and Attention to Detail, Bachelor's degree in computer science, Data Analytics, Statistics, Information Systems, or a related field. Approximately 2-4 years of experience in analytics engineering, business intelligence, or data analysis. Prior analytics experience in a manufacturing company. Demonstrated experience with Power BI data modeling, DAX, Power Query, dashboard development, and performance optimization., To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required: Experience working with Manufacturing and Supply Chain KPIs. Experience working with ERP, MRP, MES, or other manufacturing systems and their underlying data preferred. Strong proficiency in SQL for querying, transformation, and data modeling. Solid understanding of data quality concepts, data cleaning techniques, and data modeling fundamentals. Experience mapping and integrating data across multiple source systems. Experience with additional business intelligence tools is preferred. Exposure to data engineering practices, including pipeline development, ETL/ELT, version control, and orchestration, is preferred. Familiarity with cloud data warehouses is preferred. Strong documentation habits and attention to detail. ## Description The Data Analyst evaluates, cleans, integrates, and analyzes data from multiple business systems. This position develops and maintains trusted analytical datasets, dashboards, and reports; documents data lineage and transformation logic; monitors reporting accuracy and performance; and recommends improvements that make organizational data more consistent, accessible, and useful., Evaluate existing data sources for accuracy, completeness, and consistency. Identify, document, and remediate data quality issues. Establish and maintain data validation checks and quality standards. Map disparate data sources using common keys and shared dimensions. Build and maintain transformation logic that joins and reconciles data across systems. Document data lineage, mappings, relationships, and transformation logic for reference and reproducibility. Design, build, and maintain dashboards, reports, and analytical datasets in Power BI and other business intelligence tools as needed. Support continuous improvement initiatives by identifying data-driven opportunities to improve operational efficiency, quality, cost, and delivery performance. Define and maintain consistent KPI definitions, calculation logic, and reporting standards across departments. Update and enhance analytics as business logic, source systems, or requirements evolve. Monitor reports and data pipelines for accuracy and performance and troubleshoot issues as they arise. Recommend improvements to data models, metrics, and reporting based on observed gaps and opportunities. Proactively identify ways to make data more consistent, accessible, and useful across the organization.